1. Technical Field
The present invention relates to data transformation, and more particularly to an energy saving data transform method and a data transformer.
2. Description of the Related Art
Data transform is a widely used technique in management software for transforming data from one representative form to another. For example, an overall software upgrade includes an overall database upgrade, and with each software being different in regards to its background database architecture and data storage form, data often needs to be imported, exported and transformed. Furthermore, for example, due to the increasing amounts of data, the original data architecture design becomes unwieldy and cannot satisfy the requirements of various aspects. Due to the replacement of the database and the data structure, a data transform is needed. A data transform is particularly important in the process of integrating data from different products to realize integration of software products.
Since a data transform consumes many system resources, reducing system energy consumption during the process becomes a critical problem. At present, researchers have developed many energy-saving techniques which can be divided into two categories: dynamic techniques and static techniques. The static techniques enable the system to enter a low power consumption state by setting a low power consumption operation mode. For example, clocks or power supplies of different components inside the chip are provided with a low power consumption mode switch. However, the static mode cannot dynamically adjust the resource energy consumption according to the real time usage conditions of the resources. The dynamic techniques predict future load conditions according to a history load of the system and dynamically scale operating frequency and voltage of the chip, thereby saving energy, for example using the Dynamic Voltage and Frequency Scaling (DVFS) technique. A pitfall of the dynamic techniques, however, is that they need to predict the next load according to the historical load, and different predicting algorithms vary greatly in accuracy. In addition, there often exists a relatively large deviation between the historical load and the actual load, so the predicted result in an actual application can be very inaccurate.